Visual experience

…study, we trained and tested mice on visual texture classification problems, where they needed to generalize at test time based on mid-level visual features. After learning, mice consistently discriminated the training stimuli at a high accuracy, while their performance on test stimuli varied systematically across categories. To determine a possible neural basis f

In adult mouse visual cortex, neuronal responses to visual stimuli undergo gradual and continuous changes over time. This phenomenon called “representational drift” has also been observed in other sensory and higher cortical areas and has profound implications for coding strategies in the brain.It is unknown which tuning feature changes of individual neurons underlie representational drift in the primary visual cortex (V1), and what regulates their drift. Using repeated 2-photon calcium imaging in awake mice, we show a slow, time-dependent drift of the preferred orientation of V1 neurons over

Despite her still poor visual acuity and minimal visual experience, a 2-3 month old baby will reliably respond to facial expressions, smiling back at her caretaker or older sibling. But what if that same baby had been deprived of her early visual experience? Will she be able to appropriately respond to seemingly mundane interactions, such as a peer’s facial expression, if she begins seeing at the age of 10? My work is part of Project Prakash, a dual humanitarian/scientific mission to identify and treat curably blind children in India and then study how their brain learns to make sense of the v

Vision and visual cognition is experience-dependent with likely multiple sensitive periods, but we know very little about statistics of visual experience at the scale of everyday life and how they might change with development. By traditional assumptions, the world at the massive scale of daily life presents pretty much the same visual statistics to all perceivers. I will present an overview our work on ego-centric vision showing that this is not the case. The momentary image received at the eye is spatially selective, dependent on the location, posture and behavior of the perceiver. If a p

During development, the complex neuronal circuitry of the brain arises from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neuronal activity plays a critical role in shaping circuits for behavior. Current AI technologies are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these lea

…Experience-dependent plasticity presumably exploits temporal contingencies between sensory inputs to build these internal representations. However, the precise mechanisms underlying plasticity remain elusive. We derive a local synaptic plasticity model inspired by self-supervised machine learning techniques that shares a deep conceptual connection to Bienenstock-Coo

During development, the complex neuronal circuitry of the brain emerges from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neural activity plays a critical role in shaping neural circuits for behavior. Current AI techniques are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these

…or eye diseases changes in visual perception occur. It has been suggested that age-related changes are due to a general decline of cognitive, perceptual and s

Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning. At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient. In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data. Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes th